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Title:Preučevanje sekundarne strukture proteinov z računalniškimi simulacijami molekularne dinamike in nevronskimi mrežami : doktorska disertacija
Authors:ID Broz, Matic (Author)
ID Bren, Urban (Mentor) More about this mentor... New window
Files:.pdf DOK_Broz_Matic_2024.pdf (18,05 MB)
MD5: F761A1C0284162B759986CE0C57176B8
 
Language:Slovenian
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FKKT - Faculty of Chemistry and Chemical Engineering
Abstract:V doktorski disertaciji smo s pomočjo najsodobnejših računalniških metod simulacij molekulske dinamike, nevronskih mrež in molekulskega sidranja preučevali in pojasnili sekundarno strukturo proteinov pod vplivom specifičnih mutacij, strukturo proteinov pod vplivom mikrovalovnega sevanja, napovedovali dihedralne kote fi in psi proteinskega ogrodja in analizirali vlogo nevropilinov pri različnih fizioloških in patoloških procesih. V prvem delu doktorske disertacije smo s pomočjo simulacij molekulske dinamike podrobno preučili vpliv polimorfizma rs4880 (mutacija Ala16Val) na sekundarno strukturo mitohondrijske tarčne sekvence človeškega encima mangan superoksidne dismutaze. Simulacije so pokazale, da alaninska varianta ohranja stabilno α-heliksno strukturo, kar je ugodno za pravilen transport v mitohondrije. Nasprotno pa se α-heliks valinske variante razgradi, kar vodi do tvorbe β-lista in s tem potencialno moti transport. Naši rezultati podpirajo predhodne eksperimentalne ugotovitve, da ima alaninska verzija višjo aktivnost tega encima v mitohondrijih. Ugotovitve pomembno prispevajo k razumevanju povezave med strukturo in funkcijo mitohondrijske tarčne sekvence ter vpliva polimorfizma Ala16Val na aktivnost manganove superoksidne dismutaze. V drugem delu doktorske disertacije smo razvili enostaven model globokega učenja za napovedovanje dihedralnih kotov fi (ϕ) in psi (ψ) proteinskega ogrodja zgolj na podlagi primarne strukture beljakovin. Model popolnoma povezanega nevronskega omrežja z drsečim oknom velikosti 21 aminokislinskih ostankov je dosegel zadovoljivo natančnost pri napovedovanju ϕ kotov in nekoliko nižjo, a še vedno sprejemljivo natančnost pri napovedovanju ψ kotov. Pokazali smo, da je mogoče tudi z enostavnim modelom nevronskih mrež doseči visoko natančnost pri napovedovanju dihedralnih kotov proteinskega ogrodja. V tretjem delu doktorske disertacije smo s pristopom molekularne dinamike preučevali vpliv mikrovalovnega sevanja na zvijanje beljakovin in možnost napačnega zvitja. Rezultati so pokazali, da mikrovalovno segrevanje povzroči pomik proti bolj kompaktnim konformacijam proteinov, kar se odraža v predvsem v manjših radijih sukanja. Mikrovalovno sevanje pa ni imelo večjega vpliva na sekundarne strukture beljakovin na skali 200 nanosekund. Naše delo predstavlja pomemben prispevek k razumevanju posledic izpostavljenosti beljakovin mikrovalovnemu sevanju. V četrtem delu doktorske disertacije smo temeljito raziskali vlogo nevropilinov v različnih fizioloških in patoloških procesih, kot so COVID-19, rak, nevropatska bolečina, srčno-žilne bolezni in diabetes. Pregledali smo terapevtske možnosti modulacije nevropilinov in predstavili serijo antagonistov, znanih kot zaviralcev signalizacije VEGF-A in rasti tumorjev. Ugotovitve predstavljajo trdne temelje za nadaljnji razvoj učinkovitih zdravil za klinično uporabo, s poudarkom na majhnih molekulskih antagonistih. Disertacija predstavlja izvirni znanstveni prispevek na področju molekularnega modeliranja, globokega učenja in strukturne biologije z neposredno uporabnostjo pri razumevanju in zdravljenju različnih bolezni ter razvoju terapevtskih strategij.
Keywords:molekulska dinamika, nevronske mreže, struktura proteinov, dihedralni koti, mikrovalovno sevanje, nevropilini
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Broz]
Year of publishing:2024
Number of pages:X, 183 str.
PID:20.500.12556/DKUM-88736 New window
UDC:577.112.012:[004.94+004.8.032.26](043.3)
COBISS.SI-ID:210768387 New window
Publication date in DKUM:09.10.2024
Views:193
Downloads:103
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FKKT
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:22.05.2024

Secondary language

Language:English
Title:Studying the secondary structure of proteins with molecular dynamics computer simulations and neural networks
Abstract:This PhD thesis employed cutting-edge computational methods in molecular dynamics simulations, neural networks, and molecular docking to investigate and elucidate protein secondary structure under the influence of specific mutations, under microwave irradiation, to predict phi and psi dihedral angles of the protein backbone and to analyse the role of neuropilins in various physiological and pathological processes. In the first part of the dissertation, molecular dynamics simulations were used to thoroughly examine the impact of the rs4880 polymorphism (Ala16Val mutation) on the secondary structure of the mitochondrial targeting sequence of the human enzyme manganese superoxide dismutase. These simulations revealed that the alanine variant maintains a stable α-helical structure, which is beneficial for a proper transport into the mitochondria. Conversely, the α-helix of the valine variant breaks down, leading to the formation of a β-sheet, thus potentially disrupting transport. Our findings support previous experimental observations that the alanine version ensures higher activity of this enzyme within the mitochondria. The obtained findings significantly contribute to the understanding of the structure-function relationship of the mitochondrial targeting sequence and the impact of the Ala16Val polymorphism on the activity of manganese superoxide dismutase. The second part of the dissertation developed a simple deep-learning model for predicting the phi (ϕ) and psi (ψ) dihedral angles of the protein backbone solely based on the protein's primary structure. The fully connected neural network model with a sliding window size of 21 amino-acid residues achieved satisfactory accuracy in predicting ϕ angles and slightly lower, but still acceptable, accuracy in predicting ψ angles. We demonstrated that even with a simple neural network model, high accuracy can be achieved in predicting dihedral angles of the protein backbone. In the third part of the dissertation, the influence of microwave irradiation on protein folding and the possibility of misfolding were investigated using a molecular dynamics approach. The results showed that microwave heating causes a shift towards more compact protein conformations, which is reflected primarily in lower radii of gyration. However, microwave radiation did not have a significant impact on protein secondary structures on the timescale of 200 nanoseconds. Our work presents a valuable contribution to understanding the consequences of exposing proteins to microwave irradiation. The fourth part of the dissertation thoroughly investigated the role of neuropilins in various physiological and pathological processes such as COVID-19, cancer, neuropathic pain, cardiovascular diseases, and diabetes. We reviewed therapeutic options for neuropilin modulation and presented a series of antagonists known as VEGF-A signalling and tumour growth inhibitors. The findings lay a solid foundation for further development of effective drugs for clinical use, with an emphasis on small molecule antagonists. This dissertation presents an original scientific contribution to the fields of molecular modelling, deep learning, and structural biology, with direct applicability on understanding and treating various diseases as well as developing novel therapeutic strategies.
Keywords:molecular dynamics, neural networks, protein structure, dihedral angles, microwave radiation, neuropilins


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